Bibliographic record
Abstract
The objective of this study was to uncover the elements of successful medical interviews so that they can be easily shared with health educators, learners, and practitioners. The medical interview is still considered the most effective diagnostic tool available to physicians today, despite decades of rapid advancements in medical technology. When the physician-patient interaction is successful, outcomes are improved. Semi-structured interviews were conducted using an Appreciative Inquiry approach, which seeks to uncover strengths from positive experiences. The inquiry sought to identify the elements that comprise the participating physicians' most successful patient interviews. Subsequent qualitative analysis revealed eight themes: social support, mutual respect, trust, active listening, relationships, nonverbal cues, empathy, and confidentiality. These themes do not each exist separately or in a vacuum from one another; they are in fact strongly interconnected and equally important. For instance, if a physician and a patient cannot at least maintain mutual respect, then building a relationship, or even trust, is impossible. Given the qualitative nature of this study, future quantitative research should seek to validate the results. As patients assume a more participatory role in modern medical encounters, communication and other soft skills will be key in satisfying patients and improving their medical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".